Online Master’s in Data Analytics Curriculum

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Maryville University Online Master’s in Data Analytics Curriculum

The online Master of Science in Data Analytics at Maryville University aims to prepare students for professional success in the field of data analytics.

You can complete your Maryville University online Master of Science in Data Analytics in just 30 credit hours. Start at the right time for you by choosing from six admission points throughout the year. Explore the curriculum below to see how you can complete your master’s in data analytics in as little as one year of full-time or two years of part-time study.

Foundational Courses

Admission Prerequisite: Depending on your background, a foundational course in statistics may be required. BUS-501, Survey of Business, will be required if your GPA is below a 3.0 and/or if your undergraduate degree was outside the area of business; however, credits earned in foundational courses (such as BUS 241 and BUS 501) are considered prerequisites to courses required for the graduate degree.

BDAT 600Data Analytics 13 Credits
BDAT 605Database Principles3 Credits
BDAT 610Introduction to Business Data Analytics3 Credits

Descriptive Analytics

BDAT 615Data Analytics II3 Credits
BDAT 620Data Warehousing3 Credits

Predictive Analytics

BDAT 625Data Mining3 Credits
BDAT 630Data Visualization3 Credits
BDAT 640Forecasting and Predictive Modeling3 Credits

Prescriptive Analytics

BDAT 635Advanced Topics in Data Analytics3 Credits
BDAT 650Data Analytics Capstone3 Credits

Graduate Student Orientation and Conditional Admission Courses

BUS 500Graduate Preparation
BUS 501Survey of Business*
BUS 541Business Statistics3 Credits

To ensure the best possible educational experience for our students, we may update our curriculum to reflect emerging and changing employer and industry trends.

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Skills, Concepts, or Opportunities Gained with a Master’s Degree in Data Analytics

A typical master’s in data analytics curriculum consists of courses that can give students in-depth knowledge and skills in several aspects of data analytics. Many of these data analytics courses will cover the following skills, concepts, or opportunities:

  • Looking for trends, making decisions, and identifying opportunities. More than ever, businesses and organizations are using large amounts of data to make decisions, increase revenue, and find efficiencies; however, all of that data is meaningless without proper analysis. It is critical that students in this field learn how to look for patterns and trends within the data that can signal opportunities or threats and drive decision-making.
  • Combining operational data with analytical tools. Operational data, which includes data on competitors, suppliers, and finances, can be turned into meaningful information with the right analytical tools. This analysis can, in turn, help improve existing operations.
  • Presenting complex and competitive information. The amount of data at the fingertips of individuals, organizations, and businesses is staggering. As such, it’s critically important for data analytics professionals to be able to present this information in such a way that other stakeholders — company leadership, for example — can understand it. It’s not enough to just analyze the data; people working in data analytics must also be able to effectively communicate their findings.

Common Courses for MS in Data Analytics Students

These are some of the common courses offered for a data analytics degree. Though actual course titles may vary depending on the university, many data analytics programs offer courses that touch on the following concepts:

Data Analytics. The proper use of data, quantitative analysis, and modeling is driving an increasing number of business decisions. All data analytics students need to be comfortable with analyzing different types of data, using different programming languages, and drawing actionable insights from what they discover.

Database Principles. Much of the data that needs to be analyzed is housed in databases. Becoming familiar with database tools and architecture and relevant security issues is essential for data analytics professionals.

Data Visualization. Looking for and finding meaningful insights in large amounts of data is only half of the job — aspiring data analytics professionals must also be able to visualize the data in a meaningful way in order to inform business decision-making. Common forms of data visualization include charts, graphs, and maps.

Forecasting and Predictive Modeling. The field of predictive analytics is growing quickly within data analytics. Businesses often use forecasting and predictive modeling in order to best predict what may happen in the future.

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At Maryville, admission is streamlined for your convenience. You can get started by filling out an application online. It’ll only take a minute, and we’ll walk you through each step.